QuantumClone: One step analysis function
In QuantumClone: Clustering Mutations using High Throughput Sequencing (HTS) Data

Sequentially calls a function to test all accessible cellularities for all mutations in the samples,then cluster them, and finally draws phylogenetic trees based on the uncovered cellularities

QuantumClone(SNV_list, FREEC_list = NULL, contamination, nclone_range = 2:5,
  clone_priors = NULL, prior_weight = NULL, simulated = FALSE,
  save_plot = TRUE, epsilon = NULL, Initializations = 2,
  preclustering = "FLASH", timepoints = NULL, ncores = 1,
  output_directory = NULL, model.selection = "BIC", optim = "default",
  keep.all.models = FALSE, force.single.copy = FALSE)

`SNV_list`	A list of dataframes (one for each sample), with as columns : (for the first column of the first sample the name of the sample), the chromosome "Chr",the position of the mutation "Start", the number of reads supporting the mutation "Alt", the depth of coverage at this locus "Depth", and if the output from FREEC for the samples are not associated, the genotype "Genotype".
`FREEC_list`	list of dataframes from FREEC for each samples (usually named Sample_ratio.txt), in the same order as SNV_list
`contamination`	Numeric vector describind the contamination in all samples (ranging from 0 to 1). Default is 0. No longer used for clustering.
`nclone_range`	A number or range of clusters that should be used for clustering
`clone_priors`	List of vectors with the putated position of clones
`prior_weight`	Numeric with the proportion mutations in each clone
`simulated`	Should be TRUE if the data has been been generated by the QuantumCat algorithm
`save_plot`	Should the plots be saved? Default is TRUE
`epsilon`	Stop value: maximal admitted value of the difference in cluster position and weights between two optimization steps. If NULL, will take 1/(average depth).
`Initializations`	Number of initial conditions to be tested for the EM algorithm
`preclustering`	The type of preclustering used for priors: "Flash","kmedoid" or NULL. NULL will generate centers using uniform distribution. WARNING: overrides priors given
`timepoints`	a numeric vector indicating if the samples are from different timepoints or tumors (e.g. one tumor and metastates) If NULL, all samples are considered from the same tumor.
`ncores`	Number of cores to be used during EM algorithm
`output_directory`	Path to output directory
`model.selection`	The function to minimize for the model selection: can be "AIC", "BIC", or numeric. In numeric, the function uses a variant of the BIC by multiplication of the k*ln(n) factor. If >1, it will select models with lower complexity.
`optim`	use L-BFS-G optimization from R, with exact gradient, ("default"), or L-BFS-G with numerical gradient computation from optimx ("optimx"), or Differential Evolution ("DEoptim"), or a mixture of EM with exact center computation (if fully diploid), or "optim" if this fails ("compound"). Note that DEoptim is the only one that does not use EM algorithm
`keep.all.models`	Should the function output the best model (default; FALSE), or all models tested (if set to true)
`force.single.copy`	Should all mutations in overdiploid regions set to single copy? Default is FALSE

Mutations<-QuantumClone::Input_Example
 for(i in 1:2){
 Mutations[[i]]<-cbind(rep(paste("Example_",i,sep=""),times=10),Mutations[[i]])
 colnames(Mutations[[i]])[1]<-"Sample"
}
print("The data should look like this:")
print(head(Mutations[[1]]))

cat("Cluster data: will try to cluster between 3 and 4 clones, with 1 maximum search each time,
      and will use priors from preclustering.")
print("The genotype is provided in the list frame, and
          there is no associated data from FREEC to get genotype from.")
print("The computation will run on a single CPU.")
Clustering_output<-QuantumClone(SNV_list = Mutations,
FREEC_list = NULL,contamination = c(0,0),nclone_range = 3:4,
clone_priors = NULL,prior_weight = NULL ,
Initializations = 1,preclustering = "FLASH", simulated = TRUE,
save_plot = TRUE,ncores=1,output_directory="Example")
print("The data can be accessed by Clustering_output$filtered_data")
print("All plots are now saved in the working directory")